More than 10% of women in the western world contract breast cancer, and the success and ease of treatment is highly dependent on early diagnosis. Mammography is the use of low-dose x-ray radiation to image the tissue inside the breast. The technique is used to screen for and diagnose breast cancer by detecting tumors or other changes in breast tissue and aids in early detection of malignant tumors, which improves chances of successful treatment. It can identify abnormalities before a lump can be felt and provides the only reliable method of locating abnormal growths in the milk ducts. Thus it may facilitate locating suspected tumors, prior to a biopsy or surgery.
In consequence of the dangers of breast cancer and the success of mammography, the guidelines laid by the U.S. Department of Health and Human Services (HHS), the American Cancer Society (ACS), the American Medical Association (AMA) and the American College of Radiology (ACR) recommend that screening mammograms be performed annually for all women over the age of 40 in good health, with annual mammograms being advisable at earlier ages for women with a family history of breast cancer or having had prior breast biopsies.
In mammography, the breast is compressed between two plates and exposed to X-rays. Two pictures of each breast are generally taken during a screening mammogram, with extra images from different angles being sometimes necessary for women with breast implants. With so many scans requiring analysis, it is essential to automate the analysis as much as possible and to optimize the examination of the medical images, both by increased accuracy of the analysis and by faster processing times.
Now the size and shape of the breast is highly variable between women and the thickness of the compressed tissues being imaged differs significantly between subjects. The tissue composition of the breast is also highly variable and therefore the average absorption of X-rays by the breast tissue varies significantly between women.
The conventional approach to automated analysis of breast X-ray images is segmentation to determine the outline of the breast followed by analysis of suspect regions shown within the outline, to ascertain whether they are benign or malignant. The density, size and texture of breasts are so very variable that determination of the boundary of the breast in x-ray images is not easy. Categorizing suspect regions as tumors and ascertaining them as benign, malignant, uncertain or false objects or artifacts is not trivial. The shape and size of the tumors varies considerably. Asymmetry and indistinct boundaries on one edge of the suspect region are indicative of malignancy. Early diagnosis is very important as the chances of a cure are highly dependent on early treatment.
Digital mammography is preferably to conventional film in that better contrast is available. Digital mammogram images are stored as digital pictures which can be transmitted easily for remote consultation.
Compared to other anatomical regions, the breast has very low physical contrast because it is composed completely of soft tissues. In general, the breast consists of a background of fat surrounding the slightly denser, glandular structures and pathologic tissues or cysts if they are present. Typical breast calcifications are very small and thin and produce low physical contrast despite calcium being somewhat denser and having a higher atomic number than the elements of soft tissues.
Mammography is generally performed with a spectrum containing photons within a relatively narrow energy range (19 keV-21 keV) in an attempt to obtain high contrast with minimal dosage. The spectrum is produced using the characteristic radiation from a molybdenum anode x-ray tube and filtered by either a molybdenum or a rhodium filter.
The molybdenum anode, molybdenum filter system is quite good for general mammography in that it provides a spectrum that is very close to the optimum spectrum for smaller and less dense breasts. Many mammography machines give the operator the opportunity of selecting between molybdenum and rhodium filters, the latter being useful when imaging denser breasts.
Some systems have dual track anodes so that either molybdenum or rhodium can be selected as the anode material. Because of its higher atomic number (Z) rhodium produces characteristic x-radiation with higher energies than molybdenum. When the rhodium anode is selected, the beam penetration is increased. Generally, this produces better results when imaging dense breast. Since the physical parameters of X-ray sources used for mammography vary between different systems, a high variability is introduced between mammography images which is an artifact of the imaging parameters and not a result of different physiologies.
Although the magnification, brightness, contrast and orientation can be altered in digital X-ray images to display the breast tissue more clearly, such image enhancement techniques are required to be extensively automated by simple procedures to enable fast and accurate diagnosis.
In order to assist radiologists in diagnosing breast cancer from mammography images, Computer Aided Detection (CAD) of suspect regions has been introduced and is used at a growing number of clinical sites.
CAD systems for mammography, and indeed, for determining lung cancer as well, are based essentially on five basic processing steps:
(1) Segmentation of the organ to be analyzed;
(2) Location of lesion candidates;
(3) Extraction of the boundaries of lesion candidates;
(4) Extraction of feature parameters of lesion candidates, and
(5) Discrimination between lesions and false candidates using feature based classifiers.
In the early stages of the CAD process, many lesion candidates are generated. In order for the CAD process to reach a high level of performance (sensitivity and specificity), the probability of malignancy for each lesion candidate needs to be evaluated as accurately as possible. This evaluation is achieved by using a large number of quantitative features that are generally extracted from pixel values in the area of the candidate in the image. Using various statistical methods, the extracted features are combined by a classifier and each lesion candidate is then either validated or discarded.
The continuous efforts for improving the performance of the CAD algorithms for mammography, lung cancer diagnosis, and other applications, focus generally on extracting new features and in modifying the way the features are currently extracted in order to raise their statistical significance. The performance of the CAD algorithms may also be improved through a more effective combination of the extracted features in the classifier. Other tissues and organs may become cancerous, and the diagnoses of tumors therein have analogous problems to a greater or lesser degree.
It is known that malignant lesions tend to appear more often in certain regions of the breast. This knowledge is used by radiologists in analyzing mammograms. There is a need to prove the efficiency, i.e. both the throughput and accuracy, of CAD analysis for medical diagnostic purposes, particularly to further automate the analysis of mammograms to improve throughput and accuracy of diagnosis, and the present invention addresses this need.